MA304: Final report

Introduction

This report covers the data analysis, interpretation of the findings and data visualisation of the data set available here

The link to this project can be found on my Github here

Section 1: Loading the data set and libraries

In the code below, Original dataset is referred to as ‘df’, Copy of the dataset used for cleaning is referred to as ‘datacopy’.

Loading the libraries

# loading the libraries
library(funModeling)
library(dplyr)
library(ggplot2)
library(plotrix)
library(chron)
library(corrplot)
library(sf)
library(mapview)

Loading the file

df <- read.csv("37-00049_UOF-P_2016_prepped.csv")

datacopy <- data.frame(df)
attach(datacopy)

str(datacopy)

## 'data.frame':    2384 obs. of  47 variables:
##  $ INCIDENT_DATE                               : chr  "OCCURRED_D" "9/3/16" "3/22/16" "5/22/16" ...
##  $ INCIDENT_TIME                               : chr  "OCCURRED_T" "4:14:00 AM" "11:00:00 PM" "1:29:00 PM" ...
##  $ UOF_NUMBER                                  : chr  "UOFNum" "37702" "33413" "34567" ...
##  $ OFFICER_ID                                  : chr  "CURRENT_BA" "10810" "7706" "11014" ...
##  $ OFFICER_GENDER                              : chr  "OffSex" "Male" "Male" "Male" ...
##  $ OFFICER_RACE                                : chr  "OffRace" "Black" "White" "Black" ...
##  $ OFFICER_HIRE_DATE                           : chr  "HIRE_DT" "5/7/14" "1/8/99" "5/20/15" ...
##  $ OFFICER_YEARS_ON_FORCE                      : chr  "INCIDENT_DATE_LESS_" "2" "17" "1" ...
##  $ OFFICER_INJURY                              : chr  "OFF_INJURE" "No" "Yes" "No" ...
##  $ OFFICER_INJURY_TYPE                         : chr  "OFF_INJURE_DESC" "No injuries noted or visible" "Sprain/Strain" "No injuries noted or visible" ...
##  $ OFFICER_HOSPITALIZATION                     : chr  "OFF_HOSPIT" "No" "Yes" "No" ...
##  $ SUBJECT_ID                                  : chr  "CitNum" "46424" "44324" "45126" ...
##  $ SUBJECT_RACE                                : chr  "CitRace" "Black" "Hispanic" "Hispanic" ...
##  $ SUBJECT_GENDER                              : chr  "CitSex" "Female" "Male" "Male" ...
##  $ SUBJECT_INJURY                              : chr  "CIT_INJURE" "Yes" "No" "No" ...
##  $ SUBJECT_INJURY_TYPE                         : chr  "SUBJ_INJURE_DESC" "Non-Visible Injury/Pain" "No injuries noted or visible" "No injuries noted or visible" ...
##  $ SUBJECT_WAS_ARRESTED                        : chr  "CIT_ARREST" "Yes" "Yes" "Yes" ...
##  $ SUBJECT_DESCRIPTION                         : chr  "CIT_INFL_A" "Mentally unstable" "Mentally unstable" "Unknown" ...
##  $ SUBJECT_OFFENSE                             : chr  "CitChargeT" "APOWW" "APOWW" "APOWW" ...
##  $ REPORTING_AREA                              : chr  "RA" "2062" "1197" "4153" ...
##  $ BEAT                                        : chr  "BEAT" "134" "237" "432" ...
##  $ SECTOR                                      : chr  "SECTOR" "130" "230" "430" ...
##  $ DIVISION                                    : chr  "DIVISION" "CENTRAL" "NORTHEAST" "SOUTHWEST" ...
##  $ LOCATION_DISTRICT                           : chr  "DIST_NAME" "D14" "D9" "D6" ...
##  $ STREET_NUMBER                               : chr  "STREET_N" "211" "7647" "716" ...
##  $ STREET_NAME                                 : chr  "STREET" "Ervay" "Ferguson" "bimebella dr" ...
##  $ STREET_DIRECTION                            : chr  "street_g" "N" "NULL" "NULL" ...
##  $ STREET_TYPE                                 : chr  "street_t" "St." "Rd." "Ln." ...
##  $ LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION: chr  "Street Address" "211 N ERVAY ST" "7647 FERGUSON RD" "716 BIMEBELLA LN" ...
##  $ LOCATION_CITY                               : chr  "City" "Dallas" "Dallas" "Dallas" ...
##  $ LOCATION_STATE                              : chr  "State" "TX" "TX" "TX" ...
##  $ LOCATION_LATITUDE                           : chr  "Latitude" "32.782205" "32.798978" "32.73971" ...
##  $ LOCATION_LONGITUDE                          : chr  "Longitude" "-96.797461" "-96.717493" "-96.92519" ...
##  $ INCIDENT_REASON                             : chr  "SERVICE_TY" "Arrest" "Arrest" "Arrest" ...
##  $ REASON_FOR_FORCE                            : chr  "UOF_REASON" "Arrest" "Arrest" "Arrest" ...
##  $ TYPE_OF_FORCE_USED1                         : chr  "ForceType1" "Hand/Arm/Elbow Strike" "Joint Locks" "Take Down - Group" ...
##  $ TYPE_OF_FORCE_USED2                         : chr  "ForceType2" "" "" "" ...
##  $ TYPE_OF_FORCE_USED3                         : chr  "ForceType3" "" "" "" ...
##  $ TYPE_OF_FORCE_USED4                         : chr  "ForceType4" "" "" "" ...
##  $ TYPE_OF_FORCE_USED5                         : chr  "ForceType5" "" "" "" ...
##  $ TYPE_OF_FORCE_USED6                         : chr  "ForceType6" "" "" "" ...
##  $ TYPE_OF_FORCE_USED7                         : chr  "ForceType7" "" "" "" ...
##  $ TYPE_OF_FORCE_USED8                         : chr  "ForceType8" "" "" "" ...
##  $ TYPE_OF_FORCE_USED9                         : chr  "ForceType9" "" "" "" ...
##  $ TYPE_OF_FORCE_USED10                        : chr  "ForceType10" "" "" "" ...
##  $ NUMBER_EC_CYCLES                            : chr  "Cycles_Num" "NULL" "NULL" "NULL" ...
##  $ FORCE_EFFECTIVE                             : chr  "ForceEffec" " Yes" " Yes" " Yes" ...

Section 2: Cleaning the dataset

Refactoring Process

Initially We remove the extra definition row (row1) at the first index to eliminate redundancy followerd by assigning all the “” and “NULL” to NA for easier manipulation of the data down the road. We then check for missing values by using the is.na() function.

datacopy <- datacopy[-c(1),]
datacopy[datacopy == "NULL"] <- NA
datacopy[datacopy == ""] <- NA

colSums(sapply(datacopy, is.na))

##                                INCIDENT_DATE 
##                                            0 
##                                INCIDENT_TIME 
##                                           10 
##                                   UOF_NUMBER 
##                                            0 
##                                   OFFICER_ID 
##                                            0 
##                               OFFICER_GENDER 
##                                            0 
##                                 OFFICER_RACE 
##                                            0 
##                            OFFICER_HIRE_DATE 
##                                            0 
##                       OFFICER_YEARS_ON_FORCE 
##                                            0 
##                               OFFICER_INJURY 
##                                            0 
##                          OFFICER_INJURY_TYPE 
##                                            0 
##                      OFFICER_HOSPITALIZATION 
##                                            0 
##                                   SUBJECT_ID 
##                                            0 
##                                 SUBJECT_RACE 
##                                           39 
##                               SUBJECT_GENDER 
##                                           10 
##                               SUBJECT_INJURY 
##                                            0 
##                          SUBJECT_INJURY_TYPE 
##                                            0 
##                         SUBJECT_WAS_ARRESTED 
##                                            0 
##                          SUBJECT_DESCRIPTION 
##                                           76 
##                              SUBJECT_OFFENSE 
##                                            0 
##                               REPORTING_AREA 
##                                            0 
##                                         BEAT 
##                                            0 
##                                       SECTOR 
##                                            0 
##                                     DIVISION 
##                                            0 
##                            LOCATION_DISTRICT 
##                                            0 
##                                STREET_NUMBER 
##                                            0 
##                                  STREET_NAME 
##                                            0 
##                             STREET_DIRECTION 
##                                         1728 
##                                  STREET_TYPE 
##                                          143 
## LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION 
##                                            0 
##                                LOCATION_CITY 
##                                            0 
##                               LOCATION_STATE 
##                                            0 
##                            LOCATION_LATITUDE 
##                                           55 
##                           LOCATION_LONGITUDE 
##                                           55 
##                              INCIDENT_REASON 
##                                           11 
##                             REASON_FOR_FORCE 
##                                           11 
##                          TYPE_OF_FORCE_USED1 
##                                            0 
##                          TYPE_OF_FORCE_USED2 
##                                          747 
##                          TYPE_OF_FORCE_USED3 
##                                         1510 
##                          TYPE_OF_FORCE_USED4 
##                                         1996 
##                          TYPE_OF_FORCE_USED5 
##                                         2226 
##                          TYPE_OF_FORCE_USED6 
##                                         2322 
##                          TYPE_OF_FORCE_USED7 
##                                         2361 
##                          TYPE_OF_FORCE_USED8 
##                                         2378 
##                          TYPE_OF_FORCE_USED9 
##                                         2382 
##                         TYPE_OF_FORCE_USED10 
##                                         2382 
##                             NUMBER_EC_CYCLES 
##                                         2226 
##                              FORCE_EFFECTIVE 
##                                            0

Changing the dates to date format

x_dates <- as.Date(INCIDENT_DATE, "%m/%d/%Y")
x_dates <- x_dates[-1]
datacopy$INCIDENT_DATE <- x_dates
rm(x_dates)

x_dates <- as.Date(OFFICER_HIRE_DATE, "%m/%d/%Y")
x_dates <- x_dates[-1]
datacopy$OFFICER_HIRE_DATE <- x_dates
rm(x_dates)

Converting the format from character to POSIXlt

datacopy$INCIDENT_TIME <- strptime(datacopy$INCIDENT_TIME, format = "%H:%M:%S")

Converting desired variables into numeric

datacopy$OFFICER_ID <- as.numeric(datacopy$OFFICER_ID)

datacopy$SUBJECT_ID <- as.numeric(datacopy$SUBJECT_ID)

datacopy$OFFICER_YEARS_ON_FORCE <- as.numeric(datacopy$OFFICER_YEARS_ON_FORCE)

datacopy$BEAT <- as.numeric(datacopy$BEAT)

datacopy$SECTOR <- as.numeric(datacopy$SECTOR)

datacopy$REPORTING_AREA <- as.numeric(datacopy$REPORTING_AREA)

datacopy$STREET_NUMBER  <- as.numeric(datacopy$STREET_NUMBER)

datacopy$LOCATION_LATITUDE <- as.numeric(datacopy$LOCATION_LATITUDE)

datacopy$LOCATION_LONGITUDE <- as.numeric(datacopy$LOCATION_LONGITUDE)

Converting desired variables into factors

datacopy$OFFICER_GENDER <- as.factor(datacopy$OFFICER_GENDER)
summary(datacopy$OFFICER_GENDER)

## Female   Male 
##    240   2143

datacopy$OFFICER_RACE <- as.factor(datacopy$OFFICER_RACE)
summary(datacopy$OFFICER_RACE)

## American Ind        Asian        Black     Hispanic        Other        White 
##            8           55          341          482           27         1470

datacopy$SUBJECT_RACE <- as.factor(datacopy$SUBJECT_RACE)
summary(datacopy$SUBJECT_RACE)

## American Ind        Asian        Black     Hispanic        Other        White 
##            1            5         1333          524           11          470 
##         NA's 
##           39

datacopy$SUBJECT_GENDER <- as.factor(datacopy$SUBJECT_GENDER)
summary(datacopy$SUBJECT_GENDER)

##  Female    Male Unknown    NA's 
##     440    1932       1      10

datacopy$OFFICER_INJURY <- as.factor(datacopy$OFFICER_INJURY)
summary(datacopy$OFFICER_INJURY)

##   No  Yes 
## 2149  234

datacopy$SUBJECT_INJURY <- as.factor(datacopy$SUBJECT_INJURY)
summary(datacopy$SUBJECT_INJURY)

##   No  Yes 
## 1754  629

datacopy$OFFICER_HOSPITALIZATION <- as.factor(datacopy$OFFICER_HOSPITALIZATION)
summary(datacopy$OFFICER_HOSPITALIZATION)

##   No  Yes 
## 2335   48

datacopy$SUBJECT_WAS_ARRESTED <- as.factor(datacopy$SUBJECT_WAS_ARRESTED)
summary(datacopy$SUBJECT_WAS_ARRESTED)

##   No  Yes 
##  335 2048

datacopy$SUBJECT_DESCRIPTION <- as.factor(datacopy$SUBJECT_DESCRIPTION)
summary(datacopy$SUBJECT_DESCRIPTION)

##                   Alchohol Alchohol and unknown drugs 
##                        382                        280 
##                     Animal                  FD-Animal 
##                          1                          1 
##           FD-Motor Vehicle         FD-Suspect Unarmed 
##                          2                         29 
##          FD-Suspect w/ Gun FD-Suspect w/ Other Weapon 
##                         36                         25 
##        FD-Unknown if Armed                  Marijuana 
##                        110                         50 
##          Mentally unstable              None detected 
##                        412                        297 
##                    Unknown              Unknown Drugs 
##                        364                        318 
##                       NA's 
##                         76

datacopy$DIVISION <- as.factor(datacopy$DIVISION)
summary(datacopy$DIVISION)

##       CENTRAL NORTH CENTRAL     NORTHEAST     NORTHWEST SOUTH CENTRAL 
##           563           319           341           191           310 
##     SOUTHEAST     SOUTHWEST 
##           362           297

datacopy$LOCATION_DISTRICT <- as.factor(datacopy$LOCATION_DISTRICT)
summary(datacopy$LOCATION_DISTRICT)

##  D1 D10 D11 D12 D13 D14  D2  D3  D4  D5  D6  D7  D8  D9 
##  84 140 160 135  93 313 310  96 222 114 213 231 174  98

datacopy$STREET_DIRECTION <- as.factor(datacopy$STREET_DIRECTION)
summary(datacopy$STREET_DIRECTION)

##    E    N    S    W NA's 
##  120  225  187  123 1728

datacopy$STREET_TYPE <- as.factor(datacopy$STREET_TYPE)
summary(datacopy$STREET_TYPE)

##      Ave     Ave.    Blvd.     Cir.      Ct.       Dr      Dr.   Expwy. 
##        3      353      160        8        5        1      321       36 
##     Frwy    Frwy.     Hwy. Intersec      Ln.    Pkwy.      Pl.      Rd. 
##        1       52       36        1      219       45        9      400 
##      Row       St      St.     Trl.      Way     NA's 
##        1        2      557       10       20      143

datacopy$INCIDENT_REASON <- as.factor(datacopy$INCIDENT_REASON)
summary(datacopy$INCIDENT_REASON)

##  Accidental Discharge                Arrest        Call for Cover 
##                     1                  1157                   131 
##     Crime in Progress         Crowd Control   Off-Duty Employment 
##                    82                     5                    51 
##     Off-Duty Incident Other ( In Narrative)       Pedestrian Stop 
##                    12                    70                    36 
##          Service Call   Suspicious Activity          Traffic Stop 
##                   673                    47                    93 
##     Warrant Execution                  NA's 
##                    14                    11

datacopy$REASON_FOR_FORCE <- as.factor(datacopy$REASON_FOR_FORCE)
summary(datacopy$REASON_FOR_FORCE)

##        Active Aggression        Aggressive Animal                   Arrest 
##                      346                        4                     1050 
##  Assault to Other Person        Barricaded Person       Crowd Disbursement 
##                       65                        7                        2 
## Danger to self or others          Detention/Frisk                    Other 
##                      347                      206                      148 
##     Property Destruction           Weapon Display                     NA's 
##                        2                      195                       11

Removal Process

We begin by removing useless columns like LOCATION_STATE, LOCATION_CITY as both of those contain a redundant value throughout the dataset. We also remove STREET_DIRECTION from the dataset as it is not of big imporatance with respect to our objective. Using the reassignment we remove the rows which have null values for columns longitude and latitude in the dataset, this makes the plotting process a whole lot smoother. Then using the str() function we compare the original messy dataset with the new clean one, we just obtained after all the peprocessing.

datacopy <- select(datacopy, -c(LOCATION_STATE, LOCATION_CITY, STREET_DIRECTION))

datacopy <- rename(datacopy, LON = LOCATION_LONGITUDE,
                   LAT = LOCATION_LATITUDE)

datacopy <- datacopy[-c(4:5, 10:18, 20:35, 37:64),]

str(datacopy)

## 'data.frame':    2328 obs. of  44 variables:
##  $ INCIDENT_DATE                               : Date, format: "0016-09-03" "0016-03-22" ...
##  $ INCIDENT_TIME                               : POSIXlt, format: "2022-04-29 04:14:00" "2022-04-29 11:00:00" ...
##  $ UOF_NUMBER                                  : chr  "37702" "33413" "34567" "36724" ...
##  $ OFFICER_ID                                  : num  10810 7706 11014 9855 9881 ...
##  $ OFFICER_GENDER                              : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 1 2 2 2 2 ...
##  $ OFFICER_RACE                                : Factor w/ 6 levels "American Ind",..: 3 6 3 6 6 3 4 3 4 6 ...
##  $ OFFICER_HIRE_DATE                           : Date, format: "0014-05-07" "0099-01-08" ...
##  $ OFFICER_YEARS_ON_FORCE                      : num  2 17 1 7 7 9 4 4 4 3 ...
##  $ OFFICER_INJURY                              : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 1 1 1 ...
##  $ OFFICER_INJURY_TYPE                         : chr  "No injuries noted or visible" "Sprain/Strain" "No injuries noted or visible" "No injuries noted or visible" ...
##  $ OFFICER_HOSPITALIZATION                     : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 1 1 1 ...
##  $ SUBJECT_ID                                  : num  46424 44324 45126 46549 47555 ...
##  $ SUBJECT_RACE                                : Factor w/ 6 levels "American Ind",..: 3 4 4 6 3 6 3 6 6 3 ...
##  $ SUBJECT_GENDER                              : Factor w/ 3 levels "Female","Male",..: 1 2 2 1 2 1 1 1 2 1 ...
##  $ SUBJECT_INJURY                              : Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 2 2 2 1 ...
##  $ SUBJECT_INJURY_TYPE                         : chr  "Non-Visible Injury/Pain" "No injuries noted or visible" "No injuries noted or visible" "No injuries noted or visible" ...
##  $ SUBJECT_WAS_ARRESTED                        : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 1 2 ...
##  $ SUBJECT_DESCRIPTION                         : Factor w/ 14 levels "Alchohol","Alchohol and unknown drugs",..: 11 11 13 13 12 11 11 11 14 13 ...
##  $ SUBJECT_OFFENSE                             : chr  "APOWW" "APOWW" "APOWW" "Assault/FV" ...
##  $ REPORTING_AREA                              : num  2062 1197 4153 1134 2049 ...
##  $ BEAT                                        : num  134 237 432 235 132 515 133 354 622 424 ...
##  $ SECTOR                                      : num  130 230 430 230 130 510 130 350 620 420 ...
##  $ DIVISION                                    : Factor w/ 7 levels "CENTRAL","NORTH CENTRAL",..: 1 3 7 3 1 4 1 6 2 7 ...
##  $ LOCATION_DISTRICT                           : Factor w/ 14 levels "D1","D10","D11",..: 6 14 11 14 6 11 6 13 3 11 ...
##  $ STREET_NUMBER                               : num  211 7647 716 1234 511 ...
##  $ STREET_NAME                                 : chr  "Ervay" "Ferguson" "bimebella dr" "Peavy" ...
##  $ STREET_TYPE                                 : Factor w/ 21 levels "Ave","Ave.","Blvd.",..: 19 16 13 16 19 13 19 7 20 19 ...
##  $ LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION: chr  "211 N ERVAY ST" "7647 FERGUSON RD" "716 BIMEBELLA LN" "1234 PEAVY RD" ...
##  $ LAT                                         : num  32.8 32.8 32.7 32.8 32.8 ...
##  $ LON                                         : num  -96.8 -96.7 -96.9 -96.7 -96.8 ...
##  $ INCIDENT_REASON                             : Factor w/ 13 levels "Accidental Discharge",..: 2 2 2 2 2 2 2 10 10 2 ...
##  $ REASON_FOR_FORCE                            : Factor w/ 11 levels "Active Aggression",..: 3 3 3 3 3 3 7 3 7 3 ...
##  $ TYPE_OF_FORCE_USED1                         : chr  "Hand/Arm/Elbow Strike" "Joint Locks" "Take Down - Group" "Hand Controlled Escort" ...
##  $ TYPE_OF_FORCE_USED2                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED3                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED4                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED5                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED6                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED7                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED8                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED9                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED10                        : chr  NA NA NA NA ...
##  $ NUMBER_EC_CYCLES                            : chr  NA NA NA NA ...
##  $ FORCE_EFFECTIVE                             : chr  " Yes" " Yes" " Yes" " Yes" ...

str(df)

## 'data.frame':    2384 obs. of  47 variables:
##  $ INCIDENT_DATE                               : chr  "OCCURRED_D" "9/3/16" "3/22/16" "5/22/16" ...
##  $ INCIDENT_TIME                               : chr  "OCCURRED_T" "4:14:00 AM" "11:00:00 PM" "1:29:00 PM" ...
##  $ UOF_NUMBER                                  : chr  "UOFNum" "37702" "33413" "34567" ...
##  $ OFFICER_ID                                  : chr  "CURRENT_BA" "10810" "7706" "11014" ...
##  $ OFFICER_GENDER                              : chr  "OffSex" "Male" "Male" "Male" ...
##  $ OFFICER_RACE                                : chr  "OffRace" "Black" "White" "Black" ...
##  $ OFFICER_HIRE_DATE                           : chr  "HIRE_DT" "5/7/14" "1/8/99" "5/20/15" ...
##  $ OFFICER_YEARS_ON_FORCE                      : chr  "INCIDENT_DATE_LESS_" "2" "17" "1" ...
##  $ OFFICER_INJURY                              : chr  "OFF_INJURE" "No" "Yes" "No" ...
##  $ OFFICER_INJURY_TYPE                         : chr  "OFF_INJURE_DESC" "No injuries noted or visible" "Sprain/Strain" "No injuries noted or visible" ...
##  $ OFFICER_HOSPITALIZATION                     : chr  "OFF_HOSPIT" "No" "Yes" "No" ...
##  $ SUBJECT_ID                                  : chr  "CitNum" "46424" "44324" "45126" ...
##  $ SUBJECT_RACE                                : chr  "CitRace" "Black" "Hispanic" "Hispanic" ...
##  $ SUBJECT_GENDER                              : chr  "CitSex" "Female" "Male" "Male" ...
##  $ SUBJECT_INJURY                              : chr  "CIT_INJURE" "Yes" "No" "No" ...
##  $ SUBJECT_INJURY_TYPE                         : chr  "SUBJ_INJURE_DESC" "Non-Visible Injury/Pain" "No injuries noted or visible" "No injuries noted or visible" ...
##  $ SUBJECT_WAS_ARRESTED                        : chr  "CIT_ARREST" "Yes" "Yes" "Yes" ...
##  $ SUBJECT_DESCRIPTION                         : chr  "CIT_INFL_A" "Mentally unstable" "Mentally unstable" "Unknown" ...
##  $ SUBJECT_OFFENSE                             : chr  "CitChargeT" "APOWW" "APOWW" "APOWW" ...
##  $ REPORTING_AREA                              : chr  "RA" "2062" "1197" "4153" ...
##  $ BEAT                                        : chr  "BEAT" "134" "237" "432" ...
##  $ SECTOR                                      : chr  "SECTOR" "130" "230" "430" ...
##  $ DIVISION                                    : chr  "DIVISION" "CENTRAL" "NORTHEAST" "SOUTHWEST" ...
##  $ LOCATION_DISTRICT                           : chr  "DIST_NAME" "D14" "D9" "D6" ...
##  $ STREET_NUMBER                               : chr  "STREET_N" "211" "7647" "716" ...
##  $ STREET_NAME                                 : chr  "STREET" "Ervay" "Ferguson" "bimebella dr" ...
##  $ STREET_DIRECTION                            : chr  "street_g" "N" "NULL" "NULL" ...
##  $ STREET_TYPE                                 : chr  "street_t" "St." "Rd." "Ln." ...
##  $ LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION: chr  "Street Address" "211 N ERVAY ST" "7647 FERGUSON RD" "716 BIMEBELLA LN" ...
##  $ LOCATION_CITY                               : chr  "City" "Dallas" "Dallas" "Dallas" ...
##  $ LOCATION_STATE                              : chr  "State" "TX" "TX" "TX" ...
##  $ LOCATION_LATITUDE                           : chr  "Latitude" "32.782205" "32.798978" "32.73971" ...
##  $ LOCATION_LONGITUDE                          : chr  "Longitude" "-96.797461" "-96.717493" "-96.92519" ...
##  $ INCIDENT_REASON                             : chr  "SERVICE_TY" "Arrest" "Arrest" "Arrest" ...
##  $ REASON_FOR_FORCE                            : chr  "UOF_REASON" "Arrest" "Arrest" "Arrest" ...
##  $ TYPE_OF_FORCE_USED1                         : chr  "ForceType1" "Hand/Arm/Elbow Strike" "Joint Locks" "Take Down - Group" ...
##  $ TYPE_OF_FORCE_USED2                         : chr  "ForceType2" "" "" "" ...
##  $ TYPE_OF_FORCE_USED3                         : chr  "ForceType3" "" "" "" ...
##  $ TYPE_OF_FORCE_USED4                         : chr  "ForceType4" "" "" "" ...
##  $ TYPE_OF_FORCE_USED5                         : chr  "ForceType5" "" "" "" ...
##  $ TYPE_OF_FORCE_USED6                         : chr  "ForceType6" "" "" "" ...
##  $ TYPE_OF_FORCE_USED7                         : chr  "ForceType7" "" "" "" ...
##  $ TYPE_OF_FORCE_USED8                         : chr  "ForceType8" "" "" "" ...
##  $ TYPE_OF_FORCE_USED9                         : chr  "ForceType9" "" "" "" ...
##  $ TYPE_OF_FORCE_USED10                        : chr  "ForceType10" "" "" "" ...
##  $ NUMBER_EC_CYCLES                            : chr  "Cycles_Num" "NULL" "NULL" "NULL" ...
##  $ FORCE_EFFECTIVE                             : chr  "ForceEffec" " Yes" " Yes" " Yes" ...

Section 3: Exploring the data set after cleaning

Splitting the dataset into numerical and categorical variables for efficient plotting

numericVars <- which(sapply(datacopy, is.numeric))
numericVarNames <- names(numericVars)
cat('There are', length(numericVars), 'numeric variables')

## There are 9 numeric variables

catVars <- which(sapply(datacopy, is.factor))
catVarNames <- names(catVars)
cat('The are', length(catVars), 'categorical variables')

## The are 14 categorical variables

catVars

##          OFFICER_GENDER            OFFICER_RACE          OFFICER_INJURY 
##                       5                       6                       9 
## OFFICER_HOSPITALIZATION            SUBJECT_RACE          SUBJECT_GENDER 
##                      11                      13                      14 
##          SUBJECT_INJURY    SUBJECT_WAS_ARRESTED     SUBJECT_DESCRIPTION 
##                      15                      17                      18 
##                DIVISION       LOCATION_DISTRICT             STREET_TYPE 
##                      23                      24                      27 
##         INCIDENT_REASON        REASON_FOR_FORCE 
##                      31                      32

numericVars

##             OFFICER_ID OFFICER_YEARS_ON_FORCE             SUBJECT_ID 
##                      4                      8                     12 
##         REPORTING_AREA                   BEAT                 SECTOR 
##                     20                     21                     22 
##          STREET_NUMBER                    LAT                    LON 
##                     25                     29                     30

cat_df <- subset(datacopy, select = c(5, 6, 9, 11, 13, 14, 15, 17, 18, 23, 24, 27, 31, 32))
num_df <- subset(datacopy, select = c(4, 8, 12, 20, 21, 22, 25, 29, 30))

plot_num(num_df)

freq(cat_df)

##   OFFICER_GENDER frequency percentage cumulative_perc
## 1           Male      2089      89.73           89.73
## 2         Female       239      10.27          100.00

##   OFFICER_RACE frequency percentage cumulative_perc
## 1        White      1435      61.64           61.64
## 2     Hispanic       475      20.40           82.04
## 3        Black       331      14.22           96.26
## 4        Asian        54       2.32           98.58
## 5        Other        26       1.12           99.70
## 6 American Ind         7       0.30          100.00

##   OFFICER_INJURY frequency percentage cumulative_perc
## 1             No      2097      90.08           90.08
## 2            Yes       231       9.92          100.00

##   OFFICER_HOSPITALIZATION frequency percentage cumulative_perc
## 1                      No      2280      97.94           97.94
## 2                     Yes        48       2.06          100.00

##   SUBJECT_RACE frequency percentage cumulative_perc
## 1        Black      1301      55.88           55.88
## 2     Hispanic       516      22.16           78.04
## 3        White       460      19.76           97.80
## 4         <NA>        35       1.50           99.30
## 5        Other        11       0.47           99.77
## 6        Asian         4       0.17           99.94
## 7 American Ind         1       0.04          100.00

##   SUBJECT_GENDER frequency percentage cumulative_perc
## 1           Male      1893      81.31           81.31
## 2         Female       424      18.21           99.52
## 3           <NA>        10       0.43           99.95
## 4        Unknown         1       0.04          100.00

##   SUBJECT_INJURY frequency percentage cumulative_perc
## 1             No      1707      73.32           73.32
## 2            Yes       621      26.68          100.00

##   SUBJECT_WAS_ARRESTED frequency percentage cumulative_perc
## 1                  Yes      1999      85.87           85.87
## 2                   No       329      14.13          100.00

##           SUBJECT_DESCRIPTION frequency percentage cumulative_perc
## 1           Mentally unstable       401      17.23           17.23
## 2                    Alchohol       378      16.24           33.47
## 3                     Unknown       358      15.38           48.85
## 4               Unknown Drugs       312      13.40           62.25
## 5               None detected       291      12.50           74.75
## 6  Alchohol and unknown drugs       271      11.64           86.39
## 7         FD-Unknown if Armed       106       4.55           90.94
## 8                        <NA>        75       3.22           94.16
## 9                   Marijuana        47       2.02           96.18
## 10          FD-Suspect w/ Gun        33       1.42           97.60
## 11         FD-Suspect Unarmed        28       1.20           98.80
## 12 FD-Suspect w/ Other Weapon        24       1.03           99.83
## 13           FD-Motor Vehicle         2       0.09           99.92
## 14                     Animal         1       0.04           99.96
## 15                  FD-Animal         1       0.04          100.00

##        DIVISION frequency percentage cumulative_perc
## 1       CENTRAL       556      23.88           23.88
## 2     SOUTHEAST       344      14.78           38.66
## 3     NORTHEAST       332      14.26           52.92
## 4 NORTH CENTRAL       309      13.27           66.19
## 5 SOUTH CENTRAL       303      13.02           79.21
## 6     SOUTHWEST       295      12.67           91.88
## 7     NORTHWEST       189       8.12          100.00

##    LOCATION_DISTRICT frequency percentage cumulative_perc
## 1                D14       311      13.36           13.36
## 2                 D2       305      13.10           26.46
## 3                 D4       221       9.49           35.95
## 4                 D7       214       9.19           45.14
## 5                 D6       212       9.11           54.25
## 6                 D8       168       7.22           61.47
## 7                D11       151       6.49           67.96
## 8                D10       135       5.80           73.76
## 9                D12       134       5.76           79.52
## 10                D5       114       4.90           84.42
## 11                D9        94       4.04           88.46
## 12                D3        93       3.99           92.45
## 13               D13        92       3.95           96.40
## 14                D1        84       3.61          100.00

##    STREET_TYPE frequency percentage cumulative_perc
## 1          St.       549      23.58           23.58
## 2          Rd.       395      16.97           40.55
## 3         Ave.       351      15.08           55.63
## 4          Dr.       317      13.62           69.25
## 5          Ln.       219       9.41           78.66
## 6        Blvd.       151       6.49           85.15
## 7         <NA>       135       5.80           90.95
## 8        Pkwy.        44       1.89           92.84
## 9       Expwy.        36       1.55           94.39
## 10       Frwy.        35       1.50           95.89
## 11        Hwy.        35       1.50           97.39
## 12         Way        20       0.86           98.25
## 13        Trl.        10       0.43           98.68
## 14         Pl.         9       0.39           99.07
## 15        Cir.         8       0.34           99.41
## 16         Ct.         5       0.21           99.62
## 17         Ave         3       0.13           99.75
## 18          St         2       0.09           99.84
## 19          Dr         1       0.04           99.88
## 20        Frwy         1       0.04           99.92
## 21    Intersec         1       0.04           99.96
## 22         Row         1       0.04          100.00

##          INCIDENT_REASON frequency percentage cumulative_perc
## 1                 Arrest      1122      48.20           48.20
## 2           Service Call       666      28.61           76.81
## 3         Call for Cover       131       5.63           82.44
## 4           Traffic Stop        90       3.87           86.31
## 5      Crime in Progress        81       3.48           89.79
## 6  Other ( In Narrative)        68       2.92           92.71
## 7    Off-Duty Employment        51       2.19           94.90
## 8    Suspicious Activity        43       1.85           96.75
## 9        Pedestrian Stop        36       1.55           98.30
## 10     Off-Duty Incident        12       0.52           98.82
## 11     Warrant Execution        11       0.47           99.29
## 12                  <NA>        11       0.47           99.76
## 13         Crowd Control         5       0.21           99.97
## 14  Accidental Discharge         1       0.04          100.00

##            REASON_FOR_FORCE frequency percentage cumulative_perc
## 1                    Arrest      1029      44.20           44.20
## 2  Danger to self or others       341      14.65           58.85
## 3         Active Aggression       333      14.30           73.15
## 4           Detention/Frisk       202       8.68           81.83
## 5            Weapon Display       189       8.12           89.95
## 6                     Other       143       6.14           96.09
## 7   Assault to Other Person        65       2.79           98.88
## 8                      <NA>        11       0.47           99.35
## 9         Barricaded Person         7       0.30           99.65
## 10        Aggressive Animal         4       0.17           99.82
## 11       Crowd Disbursement         2       0.09           99.91
## 12     Property Destruction         2       0.09          100.00

## [1] "Variables processed: OFFICER_GENDER, OFFICER_RACE, OFFICER_INJURY, OFFICER_HOSPITALIZATION, SUBJECT_RACE, SUBJECT_GENDER, SUBJECT_INJURY, SUBJECT_WAS_ARRESTED, SUBJECT_DESCRIPTION, DIVISION, LOCATION_DISTRICT, STREET_TYPE, INCIDENT_REASON, REASON_FOR_FORCE"

Section 4: Exploratory data analysis

Plotting the variables as pie charts to visualize the assorted variables

ggplot(datacopy, aes(x = factor(1), fill = OFFICER_RACE)) +
  geom_bar(width = 1) +
  coord_polar("y")

ggplot(datacopy, aes(x = factor(1), fill = OFFICER_GENDER)) +
  geom_bar(width = 1) +
  coord_polar("y")

ggplot(datacopy, aes(x = factor(1), fill = SUBJECT_RACE)) +
  geom_bar(width = 1) +
  coord_polar("y")

ggplot(datacopy, aes(x = factor(1), fill = SUBJECT_GENDER)) +
  geom_bar(width = 1) +
  coord_polar("y")

Plotting the variables as hue filled bar plots

ggplot(datacopy) +
geom_bar(aes(x = OFFICER_YEARS_ON_FORCE)) +
scale_fill_viridis_d()

ggplot(datacopy) +
geom_bar(aes(x = SUBJECT_WAS_ARRESTED, fill = SUBJECT_RACE)) +
scale_fill_viridis_d()

ggplot(datacopy) +
geom_bar(aes(x = SUBJECT_WAS_ARRESTED, fill = OFFICER_RACE)) +
scale_fill_viridis_d()

ggplot(datacopy) +
geom_bar(aes(x = SUBJECT_RACE, fill = SUBJECT_WAS_ARRESTED)) +
scale_fill_viridis_d()

ggplot(datacopy) +
geom_bar(aes(x = SUBJECT_WAS_ARRESTED, fill = OFFICER_INJURY)) +
scale_fill_viridis_d()

Section 5: Geo spatial mapping

Visualising the data based on the longitudinal and latitudinal coordinates and other columns acting as hue (z-axis)

str(datacopy)

## 'data.frame':    2328 obs. of  44 variables:
##  $ INCIDENT_DATE                               : Date, format: "0016-09-03" "0016-03-22" ...
##  $ INCIDENT_TIME                               : POSIXlt, format: "2022-04-29 04:14:00" "2022-04-29 11:00:00" ...
##  $ UOF_NUMBER                                  : chr  "37702" "33413" "34567" "36724" ...
##  $ OFFICER_ID                                  : num  10810 7706 11014 9855 9881 ...
##  $ OFFICER_GENDER                              : Factor w/ 2 levels "Female","Male": 2 2 2 2 2 1 2 2 2 2 ...
##  $ OFFICER_RACE                                : Factor w/ 6 levels "American Ind",..: 3 6 3 6 6 3 4 3 4 6 ...
##  $ OFFICER_HIRE_DATE                           : Date, format: "0014-05-07" "0099-01-08" ...
##  $ OFFICER_YEARS_ON_FORCE                      : num  2 17 1 7 7 9 4 4 4 3 ...
##  $ OFFICER_INJURY                              : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 1 1 1 ...
##  $ OFFICER_INJURY_TYPE                         : chr  "No injuries noted or visible" "Sprain/Strain" "No injuries noted or visible" "No injuries noted or visible" ...
##  $ OFFICER_HOSPITALIZATION                     : Factor w/ 2 levels "No","Yes": 1 2 1 1 1 1 1 1 1 1 ...
##  $ SUBJECT_ID                                  : num  46424 44324 45126 46549 47555 ...
##  $ SUBJECT_RACE                                : Factor w/ 6 levels "American Ind",..: 3 4 4 6 3 6 3 6 6 3 ...
##  $ SUBJECT_GENDER                              : Factor w/ 3 levels "Female","Male",..: 1 2 2 1 2 1 1 1 2 1 ...
##  $ SUBJECT_INJURY                              : Factor w/ 2 levels "No","Yes": 2 1 1 1 1 1 2 2 2 1 ...
##  $ SUBJECT_INJURY_TYPE                         : chr  "Non-Visible Injury/Pain" "No injuries noted or visible" "No injuries noted or visible" "No injuries noted or visible" ...
##  $ SUBJECT_WAS_ARRESTED                        : Factor w/ 2 levels "No","Yes": 2 2 2 2 2 2 2 2 1 2 ...
##  $ SUBJECT_DESCRIPTION                         : Factor w/ 14 levels "Alchohol","Alchohol and unknown drugs",..: 11 11 13 13 12 11 11 11 14 13 ...
##  $ SUBJECT_OFFENSE                             : chr  "APOWW" "APOWW" "APOWW" "Assault/FV" ...
##  $ REPORTING_AREA                              : num  2062 1197 4153 1134 2049 ...
##  $ BEAT                                        : num  134 237 432 235 132 515 133 354 622 424 ...
##  $ SECTOR                                      : num  130 230 430 230 130 510 130 350 620 420 ...
##  $ DIVISION                                    : Factor w/ 7 levels "CENTRAL","NORTH CENTRAL",..: 1 3 7 3 1 4 1 6 2 7 ...
##  $ LOCATION_DISTRICT                           : Factor w/ 14 levels "D1","D10","D11",..: 6 14 11 14 6 11 6 13 3 11 ...
##  $ STREET_NUMBER                               : num  211 7647 716 1234 511 ...
##  $ STREET_NAME                                 : chr  "Ervay" "Ferguson" "bimebella dr" "Peavy" ...
##  $ STREET_TYPE                                 : Factor w/ 21 levels "Ave","Ave.","Blvd.",..: 19 16 13 16 19 13 19 7 20 19 ...
##  $ LOCATION_FULL_STREET_ADDRESS_OR_INTERSECTION: chr  "211 N ERVAY ST" "7647 FERGUSON RD" "716 BIMEBELLA LN" "1234 PEAVY RD" ...
##  $ LAT                                         : num  32.8 32.8 32.7 32.8 32.8 ...
##  $ LON                                         : num  -96.8 -96.7 -96.9 -96.7 -96.8 ...
##  $ INCIDENT_REASON                             : Factor w/ 13 levels "Accidental Discharge",..: 2 2 2 2 2 2 2 10 10 2 ...
##  $ REASON_FOR_FORCE                            : Factor w/ 11 levels "Active Aggression",..: 3 3 3 3 3 3 7 3 7 3 ...
##  $ TYPE_OF_FORCE_USED1                         : chr  "Hand/Arm/Elbow Strike" "Joint Locks" "Take Down - Group" "Hand Controlled Escort" ...
##  $ TYPE_OF_FORCE_USED2                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED3                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED4                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED5                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED6                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED7                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED8                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED9                         : chr  NA NA NA NA ...
##  $ TYPE_OF_FORCE_USED10                        : chr  NA NA NA NA ...
##  $ NUMBER_EC_CYCLES                            : chr  NA NA NA NA ...
##  $ FORCE_EFFECTIVE                             : chr  " Yes" " Yes" " Yes" " Yes" ...

mapview(datacopy,
        xcol = "LON",
        ycol = "LAT",
        zcol = "DIVISION",
        crs = 4326,
        grid = FALSE,
        map.types = "Stamen.Toner")

mapview(datacopy,
        xcol = "LON",
        ycol = "LAT",
        zcol = "LOCATION_DISTRICT",
        crs = 4326,
        grid = FALSE,
        map.types = "Stamen.Toner")

mapview(datacopy,
        xcol = "LON",
        ycol = "LAT",
        zcol = "OFFICER_RACE",
        crs = 4326,
        grid = FALSE,
        map.types = "Stamen.Toner")

mapview(datacopy,
        xcol = "LON",
        ycol = "LAT",
        zcol = "STREET_TYPE",
        crs = 4326,
        grid = FALSE,
        map.types = "Stamen.Toner")

Section 6: Interpretation of the findings

In Section 1 we load the data set, save a copy of the data to experiment on so that it does not contaminate. For preliminary analysis and checking the dimension and the type of the variables, we use the str() function, that tells us that all the variables are of the datatype character, so we assign proper data types to the variables before any further analysis.

In Section 2, we clean the data set by assigning the right data types to the variables when viewwed using str() function. New kinds of data types like ‘Date’, ‘Time’, ‘Numeric’ and ‘Factors’ are introduced.

In Section 3, we explore the data set after cleaning and dividing the data set into 2 further data frames based on their type - categorical and numerical data. Using these variables and the ‘funmodelling’ library, we obtain multiple insightful plots which plot these very variables against their frequency and also show the percentages. The STREET_TYPE ‘St’ has quite a lot of crime, maybe deploying a greater number of force on those streets will help reduce the number. Similarly for sector D14 and D2, increased number of officers could impact positively in reduction of crime. As shown in the division plot in preliminary analysis, the central part of Dallas is heavily prone to crime, maybe something could be done in that very region to reduce the same.

In Section 4, we further visualise the variables separately using different charts like pie charts and bar plots. The pie charts are easy to read and informative source of information for quick analysis.

In Section 5, we plot the map of Dallas, using the longitudes and latitudes provided in the data set. We also plot variables on the map obtained to visualise how different areas differ to each other based on all the other aspects. We also see that quite a lot of subjects were arrested without an officer injury, on further analysis on that we do observe an indication of racial bias again. Further more when SUBJECT_RACE is plotted against SUBJECT_WAS_ARRESTED we observe that however the number of arrests are quite high, the SUBJECT_RACE indicates that the most arrests made were on people of colour confirming the suspicion of a racial bias in the data set.

Also, the plot where OFFICER_RACE is plotted against SUBJECT_RACE, we see a contrast in the number for the white officers. A thing which might help in future could be adding more police officers of colour to the force in turn reducing the racial bias if not eradicating it.

Section 7: Conclusion

From all the analysis and visulaisation performed above we notice, that the data hints towards a racial bias when it comes to subject race. That number was greater for people of colour starting from ‘SUBJECT_WAS_ARRESTED’ and it decreases as we reach OFFICER_RACE. Having more female officers, and officers of colour could really make a difference in the region specifically. It would definitely help in reduction of the bias and eventually crime. Also, from the numerical plot in Section 2 we notice the OFFICER_YEARS_ON_FORCE is negatively skewed meaning most of the officers are new in the department indicating towards a possibility of lack of experience in the officers.

Lastly, interactive map plots were included to show how the variables ‘STREET_TYPE’, ‘OFFICER_RACE’, ‘LOCATION_DISTRICT’ and ‘DIVISION’ affected the plotting of the latitudes and longitudes.

Given more time, I would have converted some of the categorical data into numerical useing one-hot encoding to form levels, where they can be distinguished based on the value they hold. This could provide a deeper insight into the data set revealing more trends and interesting patterns.